Third-harmonic generation and imaging with resonant Si membrane metasurface

Dielectric metasurfaces play an increasingly important role in enhancing optical nonlinear generations owing to their ability to support strong light-matter interactions based on Mie-type multipolar resonances.Compared to metasurfaces composed of the periodic arrangement of nanoparticles,inverse,so-called,membrane metasurfaces offer unique possibilities for supporting multipolar resonances,while maintaining small unit cell size,large mode volume and high field enhancement for enhancing nonlinear frequency conversion.Here,we theoretically and experimentally investigate the formation of bound states in the continuum(BICs)from silicon dimer-hole membrane metasurfaces.We demonstrate that our BIC-formed resonance features a strong and tailorable electric near-field confinement inside the silicon membrane films.Furthermore,we show that by tuning the gap between the holes,one can open a leaky channel to transform these regular BICs into quasi-BICs,which can be excited directly under normal plane wave incidence.To prove the capabilities of such metasurfaces,we demonstrate the conversion of an infrared image to the visible range,based on the Third-harmonic generation(THG)process with the resonant membrane metasurfaces.Our results suggest a new paradigm for realising efficient nonlinear photonics metadevices and hold promise for extending the applications of nonlinear structuring surfaces to new types of all-optical near-infrared imaging technologies.

Smart mid-infrared metasurface microspectrometer gas sensing system

Smart,low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications.Traditionally,electrochemical and nondispersive infrared(IR)gas sensors are designed to detect a single specific analyte.Although IR spectroscopy-based sensors provide superior performance,their deployment is limited due to their large size and high cost.In this study,a smart,low-cost,multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm.The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free,compact(~1 cm^(3))and lightweight(~1 g).The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present.The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100%with 100%accuracy.It also detects hazardous gases at low concentrations with an accuracy of 98.4%.Ammonia can be detected at a concentration of 100 ppm.Additionally,methyl-ethyl-ketone can be detected at its permissible exposure limit(200 ppm);this concentration is considered low and nonhazardous.This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

Fundamental limits for transmission modulation in VO_(2) metasurfaces

The interest in dynamic modulation of light by ultra-thin materials exhibiting insulator–metal phase transition,such as VO_(2),has rapidly grown due to the myriad industrial applications,including smart windows and optical limiters.However,for applications in the telecommunication spectral band,the light modulation through a thin VO_(2) film is low due to the presence of strong material loss.Here,we demonstrate tailored nanostructuring of VO_(2) to dramatically enhance its transmission modulation,reaching a value as high as 0.73,which is 2 times larger than the previous modulation achieved.The resulting designs,including free-topology optimization,demonstrate the fundamental limit in acquiring the desired optical performance,including achieving positive or negative transmission contrast.Our results on nanophotonic management of lossy nanostructured films open new opportunities for applications of VO_(2) metasurfaces.

Electrically programmable solid-state metasurfaces via flash localised heating

In the last decades,metasurfaces have attracted much attention because of their extraordinary light-scattering properties.However,their inherently static geometry is an obstacle to many applications where dynamic tunability in their optical behaviour is required.Currently,there is a quest to enable dynamic tuning of metasurface properties,particularly with fast tuning rate,large modulation by small electrical signals,solid state and programmable across multiple pixels.Here,we demonstrate electrically tunable metasurfaces driven by thermo-optic effect and flash-heating in silicon.We show a 9-fold change in transmission by<5 V biasing voltage and the modulation rise-time of<625µs.Our device consists of a silicon hole array metasurface encapsulated by transparent conducting oxide as a localised heater.It allows for video frame rate optical switching over multiple pixels that can be electrically programmed.Some of the advantages of the proposed tuning method compared with other methods are the possibility to apply it for modulation in the visible and near-infrared region,large modulation depth,working at transmission regime,exhibiting low optical loss,low input voltage requirement,and operating with higher than video-rate switching speed.The device is furthermore compatible with modern electronic display technologies and could be ideal for personal electronic devices such as flat displays,virtual reality holography and light detection and ranging,where fast,solid-state and transparent optical switches are required.

Enhanced light-matter interactions in dielectric nanostructures via machine-learning approach

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand.Metasurfaces are versatile,novel platforms for manipulating the scattering,color,phase,or intensity of light.Currently,one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters,such as various materials’properties and coupling effects,as well as the geometrical parameters.Ideally,this would require multidimensional space optimization through direct numerical simulations.Recently,an alternative,popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted method.We utilize a deep-learning approach for obtaining high-quality factor(high-Q)resonances with desired characteristics,such as linewidth,amplitude,and spectral position.We exploit such high-Q resonances for enhancedlight–matter interaction in nonlinearoptical metasurfaces and optomechanical vibrations,simultaneously.We demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation;at the same time,they also contribute to 100-fold enhancement of the amplitude of optomechanical vibrations.This approach can be further used to realize structures with unconventional scattering responses.

Nonlinear quantum spectroscopy with parity-time-symmetric integrated circuits

We propose a novel quantum nonlinear interferometer design that incorporates a passive parity-time(PT)-symmetric coupler sandwiched between two nonlinear sections where signal-idler photon pairs are generated.The PT symmetry enables efficient coupling of the longer-wavelength idler photons and facilitates the sensing of losses in the second waveguide exposed to analyte under investigation,whose absorption can be inferred by measuring only the signal intensity at a shorter wavelength where efficient detectors are readily available.Remarkably,we identify a new phenomenon of sharp signal intensity fringe shift at critical idler loss values,which is distinct from the previously studied PT symmetry breaking.We discuss how such unconventional properties arising from quantum interference can provide a route to enhancing the sensing of analytes and facilitate broadband spectroscopy applications in integrated photonic platforms.